Use of satellite spectral data in crop yield estimation surveys

Singh, Randhir; Goyal, Rajiva; Saha, Sudip; Chhikara, Raj S.

doi:10.1080/01431169208904065

Cited by 30 publications

(23 citation statements)

References 4 publications

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“…(a) Total nitrogen with NDVI(860,680) (b) Total nitrogen with NDVI(750,680) The spectral coefficient has close relativity with nitrogen supply at elongating and seeding stage of winter wheat 8,9 . The normalized differential vegetation indices (NDVI) and the differential vegetation indices (DVI) were calculated at 860 nm and 750nm with 680nm respectively, which denoted growth condition of winter wheat.…”

Section: Correlation Analysis Based On Ndvi and DVImentioning

confidence: 96%

Estimating growth status of winter wheat based on aerial images and hyperspectral data

Han

Liu

et al. 2005

SPIE Proceedings

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The aim of this paper is to estimate the growth status and yield of winter wheat using aerial images and hyperspectral data obtained by unmanned aircraft, and then to perform precision management to the crop. The test farm was divided into 48 cells. Twenty-four cells were selected as variable rate fertilization area, and the other 24 cells were used as contrast area with low fertilization in growth season. In 2004, the aerial images of winter wheat canopy were measured from an unmanned aircraft. The SPAD value of crop leaf was acquired using a SPAD-502 chlorophyll meter, and then the hyperspectral reflectance of the crop canopy was measured by a handheld spectroradiometer. The vegetation indices, NDVI and DVI, were calculated from the hyperspectral data. The characteristics of the aerial images were used to evaluate the growth status. The RGB values of all cells were calculated from aerial images. The result showed that total nitrogen had better correlation with SPAD, NDVI, DVI, and RGB. NDVI and DVI had high correlation with the growth condition, and R/(R+G+B) and G/(R+G+B) had good correlation with the growth status and yield. The variable rate fertilization based on aerial images and NDVI was executed in the experimental cells. The yield map showed that the spatial variation of the yield was reduced and the total yield was increased. While in contrast cells, the spatial variation of the yield is greater than in experimental cells because of the spatial variation of the field nutrition. Therefore, it is practical to use aerial images and hyperspectral data of the crop canopy in estimation of the crop growth status.

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Section: Correlation Analysis Based On Ndvi and DVImentioning

confidence: 96%

Estimating growth status of winter wheat based on aerial images and hyperspectral data

Han

Liu

et al. 2005

SPIE Proceedings

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“…Singh et al (1992Singh et al ( , 2002 and Singh (2003) illustrated that stratification based on the spectral vegetation index improved the efficiency of crop yield estimation significantly. In those studies, post-stratified estimators were used to combine the CCE data and the stratified satellite data for crop yield estimation.…”

Section: Introductionmentioning

confidence: 98%

A multiple-frame approach to crop yield estimation from satellite- remotely sensed data

Das

Singh

2013

International Journal of Remote Sensing

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Many studies have recently explored information from satellite-remotely sensed data (SRSD) for estimating crop production statistics. The value of this information depends on the aerial and spatial resolutions of SRSD. SRSD with fine spatial resolution is costly and aerial coverage is less. Use of multiple frames of SRSD in the estimation process of crop production can increase the precision. We propose an estimator for the average yield of wheat for the state of Haryana, India. This estimator uses information from Wide Field Sensor (WiFS) and Linear Imaging Self Scanner (LISS-III) data from the Indian Remote Sensing (IRS-1D) satellite and crop-cutting experiment data collected by probability sampling design from a list frame of villages. We find that relative efficiencies of multiple-frame estimators are high in comparison to single-frame estimators.

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“…Country-level crop yield estimation using remote sensing data has been carried out operationally for more than two decades (MacDonald and Hall 1980). Vegetation indices calculated from Thematic Mapper (TM) data have improved the accuracy of ground wheat yield surveys through stratification of primary sampling units (Singh et al 1992). Promising results have been obtained through the statistical relationship between satellite reflectance values or vegetation indices and crop yield (Thenkabail et al 1992, Jiang et al 2003, Kogan et al 2005, Weissteiner and Kü hbauch 2005.…”

Section: Introductionmentioning

confidence: 99%

Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

Fang

Liang

Hoogenboom

2011

International Journal of Remote Sensing

169

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Use of satellite spectral data in crop yield estimation surveys

Cited by 30 publications

References 4 publications

Estimating growth status of winter wheat based on aerial images and hyperspectral data

Estimating growth status of winter wheat based on aerial images and hyperspectral data

A multiple-frame approach to crop yield estimation from satellite- remotely sensed data

Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

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